scholarly journals Analysis of Kansei Data by Using a Parallel GA

2013 ◽  
Vol 25 (6) ◽  
pp. 924-934
Author(s):  
Shunya SUETSUGU ◽  
Hiroki KATAYAMA ◽  
Masataka TOKUMARU
Keyword(s):  
Parallel Ga

scholarly journals Parallel GA-based approach for microwave imaging applications

2005 ◽  
Vol 53 (10) ◽  
pp. 3118-3127 ◽  
Author(s):  
A. Massa ◽  
D. Franceschini ◽  
G. Franceschini ◽  
M. Pastorino ◽  
M. Raffetto ◽  
...  
Keyword(s):  
Microwave Imaging ◽  
Parallel Ga

scholarly journals Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhaosheng Yang ◽  
Duo Mei ◽  
Qingfang Yang ◽  
Huxing Zhou ◽  
Xiaowen Li
Keyword(s):  
Genetic Algorithm ◽  
Cloud Computing ◽  
Traffic Flow ◽  
Large Scale ◽  
Support Vector ◽  
Traffic Flow Prediction ◽  
Model Based ◽  
Flow Prediction ◽  
Svm Model ◽  
Parallel Ga

To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

scholarly journals Use of a supercomputer to advance parameter optimisation using genetic algorithms

2007 ◽  
Vol 9 (4) ◽  
pp. 319-329 ◽  
Author(s):  
Achela K. Fernando ◽  
A. W. Jayawardena
Keyword(s):  
Computational Time ◽  
Hydrological Models ◽  
Rainfall Runoff ◽  
Mean Flow ◽  
Tank Model ◽  
Serial Processing ◽  
Parameter Optimisation ◽  
Modified Genetic Algorithm ◽  
Parallel Ga ◽  
Reduced Time

Parameter optimisation is a significant but time-consuming process that is inherent in conceptual hydrological models representing rainfall–runoff processes. This study presents two modifications to achieve optimised results for a Tank Model in less computational time. Firstly, a modified genetic algorithm (GA) is developed to enhance the fitness of the population consisting of possible solutions in each generation. Then the parallel processing capabilities of an IBM 9076 SP2 computer are used to expedite implementation of the GA. A comparison of processing time between a serial IBM RS/6000 390 computer and an IBM 9076 SP2 supercomputer reveals that the latter can be up to 8 times faster. The effectiveness of the modified GA is tested with two Tank Models for a hypothetical catchment and a real catchment. The former showed that the parallel GA reaches a lower overall error in reduced time. The overall RMSE, expressed as a percentage of actual mean flow rate, improves from 31.8% in a serial processing computer to 29.5% on the SP2 supercomputer. The case of the real catchment – Shek-Pi-Tau Catchment in Hong Kong – reveals that the supercomputer enhances the swiftness of the GA and achieves its objective within a couple of hours.

Parallelizing Genetic Algorithms

2008 ◽  
pp. 284-307
Author(s):  
Iker Gondra
Keyword(s):  
Genetic Algorithms ◽  
Premature Convergence ◽  
Superior Performance ◽  
Ramsey Numbers ◽  
Natural Processes ◽  
Advantages And Disadvantages ◽  
Parallel Ga ◽  
Theoretical Foundations

Genetic Algorithms (GA), which are based on the idea of optimizing by simulating the natural processes of evolution, have proven successful in solving complex problems that are not easily solved through conventional methods. This chapter introduces their major steps, operators, theoretical foundations, and problems. A parallel GA is an extension of the classical GA that takes advantage of a GA’s inherent parallelism to improve its time performance and reduce the likelihood of premature convergence. An overview of different models for parallelizing GAs is presented along with a discussion of their main advantages and disadvantages. A case study: A parallel GA for finding Ramsey Numbers is then presented. According to Ramsey Theory, a sufficiently large system (no matter how random) will always contain highly organized subsystems. The role of Ramsey numbers is to quantify some of these existential theorems. Finding Ramsey numbers has proven to be a very difficult task that has led researchers to experiment with different methods of accomplishing this task. The objective of the case study is both to illustrate the typical process of GA development and to verify the superior performance of parallel GAs in solving some of the problems (e.g., premature convergence) of traditional GAs.

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